# GGIR configuration parameters

See also complementary vignettes on: General introduction to GGIR, Cut-points, Day segment analyses, Embedding externalf unctions, and Reading ad-hoc csv file formats.

The GGIR shell function takes the input arguments and groups them into parameter objects. The first section below displays all optional GGIR input argument names, the GGIR part (1, 2, 3, 4 and/or 5) they are used in, and the parameter object they are stored in. As you will see, a few parameters are not part of any parameter object because they are direct arguments of the GGIR shell function.

In the second section of this vignette you will find a description and default value for all the arguments.

# 1 Input arguments/parameters overview

Argument (parameter) | Used in GGIR part | Parameter object
--- | --- | ---
datadir | 1, 2, 4, 5 | not in parameter objects
f0 | 1, 2, 3, 4, 5 | not in parameter objects
f1 | 1, 2, 3, 4, 5 | not in parameter objects
windowsizes | 1, 5 | params_general
desiredtz | 1, 2, 3, 4, 5 | params_general
overwrite | 1, 2, 3, 4, 5 | params_general
do.parallel | 1, 2, 3, 5 | params_general
maxNcores | 1, 2, 3, 5 | params_general
myfun | 1, 2, 3 | not in parameter objects
outputdir | 1 | not in parameter objects
studyname | 1 | not in parameter objects
chunksize | 1 | params_rawdata
do.enmo | 1 | params_metrics
do.lfenmo | 1 | params_metrics
do.en | 1 | params_metrics
do.bfen | 1 | params_metrics
do.hfen | 1 | params_metrics
do.hfenplus | 1 | params_metrics
do.mad | 1 | params_metrics
do.anglex | 1 | params_metrics
do.angley | 1 | params_metrics
do.angle | 1 | params_metrics
do.enmoa | 1 | params_metrics
do.roll_med_acc_x | 1 | params_metrics
do.roll_med_acc_y | 1 | params_metrics
do.roll_med_acc_z | 1 | params_metrics
do.dev_roll_med_acc_x | 1 | params_metrics
do.dev_roll_med_acc_y | 1 | params_metrics
do.dev_roll_med_acc_z | 1 | params_metrics
do.lfen | 1 | params_metrics
do.lfx | 1 | params_metrics
do.lfy | 1 | params_metrics
do.lfz | 1 | params_metrics
do.hfx | 1 | params_metrics
do.hfy | 1 | params_metrics
do.hfz | 1 | params_metrics
do.bfx | 1 | params_metrics
do.bfy | 1 | params_metrics
do.bfz | 1 | params_metrics
do.zcx | 1 | params_metrics
do.zcy | 1 | params_metrics
do.zcz | 1 | params_metrics
do.neishabouricounts | 1 | params_metrics
actilife_LFE | 1 | params_metrics
lb | 1 | params_metrics
hb | 1 | params_metrics
n | 1 | params_metrics
do.cal | 1 | params_rawdata
spherecrit | 1 | params_rawdata
minloadcrit | 1 | params_rawdata
printsummary | 1 | params_rawdata
print.filename | 1 | params_general
backup.cal.coef | 1 | params_rawdata
rmc.noise | 1 | params_rawdata
rmc.dec | 1 | params_rawdata
rmc.firstrow.acc | 1 | params_rawdata
rmc.firstrow.header | 1 | params_rawdata
rmc.col.acc | 1 | params_rawdata
rmc.col.temp | 1 | params_rawdata
rmc.col.time | 1 | params_rawdata
rmc.unit.acc | 1 | params_rawdata
rmc.unit.temp | 1 | params_rawdata
rmc.origin | 1 | params_rawdata
rmc.header.length | 1 | params_rawdata
mc.format.time | 1 | params_rawdata
rmc.bitrate | 1 | params_rawdata
rmc.dynamic_range | 1 | params_rawdata
rmc.unsignedbit | 1 | params_rawdata
rmc.desiredtz | 1 | params_rawdata
rmc.sf | 1 | params_rawdata
rmc.headername.sf | 1 | params_rawdata
rmc.headername.sn | 1 | params_rawdata
rmc.headername.recordingid | 1 | params_rawdata
rmc.header.structure | 1 | params_rawdata
rmc.check4timegaps | 1 | params_rawdata
rmc.col.wear | 1 | params_rawdata
rmc.doresample | 1 | params_rawdata
imputeTimegaps | 1 | params_rawdata
selectdaysfile | 1, 2 | params_cleaning
dayborder | 1, 2, 5 | params_general
dynrange | 1 | params_rawdata
configtz | 1 | params_general
minimumFileSizeMB | 1 | params_rawdata
interpolationType | 1 | params_rawdata
expand_tail_max_hours | deprecated | params_general
recordingEndSleepHour | 1 | params_general
maxRecordingInterval | 1 | params_general
nonwear_approach | 1 | params_general
dataFormat | 1 | params_general
extEpochData_dateformat | 1 | params_general
metadatadir | 2, 3, 4, 5 | not in parameter objects
minimum_MM_length.part5 | 5 | params_cleaning
strategy | 2, 5 | params_cleaning
hrs.del.start | 2, 5 | params_cleaning
hrs.del.end | 2, 5 | params_cleaning
maxdur | 2, 5 | params_cleaning
max_calendar_days | 2 | params_cleaning
includedaycrit | 2 | params_cleaning
nonWearEdgeCorrection | 2 | params_cleaning
L5M5window | 2 | params_247
M5L5res | 2, 5 | params_247
winhr | 2, 5 | params_247
qwindow | 2 | params_247
qlevels | 2 | params_247
ilevels | 2 | params_247
mvpathreshold | 2 | params_phyact
boutcriter | 2 | params_phyact
ndayswindow | 2 | params_cleaning
idloc | 2, 4 | params_general
do.imp | 2 | params_cleaning
storefolderstructure | 2, 4, 5 | params_output
epochvalues2csv | 2 | params_output
do.part2.pdf | 2 | params_output
mvpadur | 2 | params_phyact
window.summary.size | 2 | params_247
bout.metric | 2, 5 | params_phyact
closedbout | 2 | params_phyact
IVIS_windowsize_minutes | 2 | params_247
IVIS_epochsize_seconds | 2 | params_247
IVIS.activity.metric | 2 | params_247
iglevels | 2, 5 | params_247
TimeSegments2ZeroFile | 2 | params_cleaning
qM5L5 | 2 | params_247
MX.ig.min.dur | 2 | params_247
qwindow_dateformat | 2 | params_247
anglethreshold | 3 | params_sleep
timethreshold | 3 | params_sleep
acc.metric | 3, 5 | params_general
ignorenonwear | 3 | params_sleep
constrain2range | 3 | params_sleep
do.part3.pdf | 3 | params_output
sensor.location | 3, 4 | params_general
HASPT.algo | 3 | params_sleep
HASIB.algo | 3 | params_sleep
Sadeh_axis | 3 | params_sleep
longitudinal_axis | 3 | params_sleep
HASPT.ignore.invalid | 3 | params_sleep
loglocation | 4, 5 | params_sleep
colid | 4 | params_sleep
coln1 | 4 | params_sleep
possible_nap_window | 5 | params_sleep
possible_nap_dur | 5 | params_sleep
do.visual | 4 | params_output
outliers.only | 4 | params_output
excludefirstlast | 4 | params_cleaning
criterror | 4 | params_output
includenightcrit | 4 | params_cleaning
relyonguider | 4 | params_sleep
relyonsleeplog | 4 | not in parameter objects
relyonguider_SleepEfficiency | 4 | params_sleep
def.noc.sleep | 4 | params_sleep
data_cleaning_file | 4, 5 | params_cleaning
excludefirst.part4 | 4 | params_cleaning
excludelast.part4 | 4 | params_cleaning
sleepwindowType | 4 | params_cleaning
excludefirstlast.part5 | 5 | params_cleaning
boutcriter.mvpa | 5 | params_phyact
boutcriter.in | 5 | params_phyact
boutcriter.lig | 5 | params_phyact
threshold.lig | 5 | params_phyact
threshold.mod | 5 | params_phyact
threshold.vig | 5 | params_phyact
boutdur.mvpa | 5 | params_phyact
boutdur.in | 5 | params_phyact
boutdur.lig | 5 | params_phyact
save_ms5rawlevels | 5 | params_output
part5_agg2_60seconds | 5 | params_general
includedaycrit.part5 | 5 | params_cleaning
frag.metrics | 5 | params_phyact
LUXthresholds | 5 | params_247
LUX_cal_constant | 5 | params_247
LUX_cal_exponent | 5 | params_247
LUX_day_segments | 5 | params_247
timewindow | 5 | params_output
save_ms5raw_format | 5 | params_output
save_ms5raw_without_invalid | 5 | params_output
do.sibreport | 5 | params_output
visualreport_without_invalid | visualreport | params_output
dofirstpage | visualreport | params_output
visualreport | visualreport | params_output
viewingwindow | visualreport | params_output

# 2 Arguments/parameters description

All information as shown below has been auto-generated and is identical to the information provided in the GGIR package pdf manual.

## 2.1 GGIR function input arguments

### mode

Numeric (default = 1:5). Specify which of the five parts need to be run, e.g., mode = 1 makes that g.part1 is run; or mode = 1:5 makes that the whole GGIR pipeline is run, from g.part1 to g.part5.

### datadir

Character (default = c()). Directory where the accelerometer files are stored, e.g., “C:/mydata”, or list of accelerometer filenames and directories, e.g. c(“C:/mydata/myfile1.bin”, “C:/mydata/myfile2.bin”).

### outputdir

Character (default = c()). Directory where the output needs to be stored. Note that this function will attempt to create folders in this directory and uses those folder to keep output.

### studyname

Character (default = c()). If the datadir is a folder, then the study will be given the name of the data directory. If datadir is a list of filenames then the studyname as specified by this input argument will be used as name for the study.

### f0

Numeric (default = 1). File index to start with (default = 1). Index refers to the filenames sorted in alphabetical order.

### f1

Numeric (default = 0). File index to finish with (defaults to number of files available).

### do.report

Numeric (default = c(2, 4, 5)). For which parts to generate a summary spreadsheet: 2, 4, and/or 5. Default is c(2, 4, 5). A report will be generated based on the available milestone data. When creating milestone data with multiple machines it is advisable to turn the report generation off when generating the milestone data, value = c(), and then to merge the milestone data and turn report generation back on while setting overwrite to FALSE.

### configfile

Character (default = c()). Configuration file previously generated by function GGIR. See details.

### myfun

List (default = c()). External function object to be applied to raw data. See package vignette for detailed tutorial with examples on how to use the function embedding: https://cran.r-project.org/package=GGIR/vignettes/ExternalFunction.pdf

## 2.2 General Parameters

### overwrite

Boolean (default = FALSE). Do you want to overwrite analysis for which milestone data exists? If overwrite = FALSE, then milestone data from a previous analysis will be used if available and visual reports will not be created again.

### acc.metric

Character (default = “ENMO”). Which one of the acceleration metrics do you want to use for all acceleration magnitude analyses in GGIR part 5 and the visual report? For example: “ENMO”, “LFENMO”, “MAD”, “NeishabouriCount_y”, or “NeishabouriCount_vm”. Only one acceleration metric can be specified and the selected metric needs to have been calculated in part 1 (see g.part1) via arguments such as do.enmo = TRUE or do.mad = TRUE.

### maxNcores

Numeric (default = NULL). Maximum number of cores to use when argument do.parallel is set to true. GGIR by default uses either the maximum number of available cores or the number of files to process (whichever is lower), but this argument allows you to set a lower maximum.

### print.filename

Boolean (default = FALSE). Whether to print the filename before before analysing it (in case do.parallel = FALSE). Printing the filename can be useful to investigate problems (e.g., to verify that which file is being read).

### do.parallel

Boolean (default = TRUE). Whether to use multi-core processing (only works if at least 4 CPU cores are available).

### windowsizes

Numeric vector, three values (default = c(5, 900, 3600)). To indicate the lengths of the windows as in c(window1, window2, window3): window1 is the short epoch length in seconds, by default 5, and this is the time window over which acceleration and angle metrics are calculated; window2 is the long epoch length in seconds for which non-wear and signal clipping are defined, default 900 (expected to be a multitude of 60 seconds); window3 is the window length of data used for non-wear detection and by default 3600 seconds. So, when window3 is larger than window2 we use overlapping windows, while if window2 equals window3 non-wear periods are assessed by non-overlapping windows.

### desiredtz

Character (default = ““, i.e., system timezone). Timezone in which device was configured and experiments took place. If experiments took place in a different timezone, then use this argument for the timezone in which the experiments took place and argument configtz to specify where the device was configured. See also https://en.wikipedia.org/wiki/Zone.tab

### configtz

Character (default = ““, i.e., system timezone). At the moment only functional for GENEActiv .bin, AX3 cwa, ActiGraph .gt3x, and ad-hoc csv file format at the moment. Timezone in which the accelerometer was configured. Only use this argument if the timezone of configuration and timezone in which recording took place are different. See also https://en.wikipedia.org/wiki/Zone.tab

### idloc

Numeric (default = 1). If idloc = 1 the code assumes that ID number is stored in the obvious header field. Note that for ActiGraph data the ID is never stored in the file header. For value set to 2, 5, 6, and 7, GGIR looks at the filename and extracts the character string preceding the first occurance of a “_” (idloc = 2), ” ” (space, idloc = 5), “.” (dot, idloc = 6), and “-” (idloc = 7), respectively. You may have noticed that idloc 3 and 4 are skipped, they were used for one study in 2012, and not actively maintained anymore, but because it is legacy code not omitted.

### dayborder

Numeric (default = 0). Hour at which days start and end (dayborder = 4 would mean 4 am).

### part5_agg2_60seconds

Boolean (default = FALSE). Wether to use aggregate epochs to 60 seconds as part of the GGIR g.part5 analysis.

### sensor.location

Character (default = “wrist”). To indicate sensor location, default is wrist. If it is hip, the HDCZA algorithm for sleep detection also requires longitudinal axis of sensor to be between -45 and +45 degrees.

### expand_tail_max_hours

Numeric (default = NULL). This parameter has been replaced for recordingEndSleepHour.

### recordingEndSleepHour

Numeric (default = NULL). Time (in hours) at which the recording should end (or later) to expand the g.part1 output with synthetic data to trigger sleep detection for last night. Using argument recordingEndSleepHour implies the assumption that the participant fell asleep at or before the end of the recording if the recording ended at or after recordingEndSleepHour hour of the last day. This assumption may not always hold true and should be used with caution. The synthetic data for metashort entails: timestamps continuing regularly, zeros for acceleration metrics other than EN, one for EN. Angle columns are created in a way that it triggers the sleep detection using the equation: round(sin((1:length_expansion) / (900/epochsize))) * 15. To keep track of the tail expansion g.part1 stores the lenght of the expansion in the RData files, which is then passed via g.part2, g.part3, and g.part4 to g.part5. In g.part5 the tail expansion size is included as an additional variable in the csv-reports. In the g.part4 csv-report the last nigth is omitted, because we know that sleep estimates from the last night will not be trustworthy. Similarly, in the g.part5 output columns related to the sleep assessment will be omitted for the last window to avoid biassing the averages Further, the synthetic data are also ignored in the visualizations and time series output to avoid biased output.

### dataFormat

Character (default = “raw”). to indicate what the format is of the data in datadir. Current alternatives: ukbiobank_csv, actiwatch_csv, and actiwatch_awd, which correspond to the 5, 15 and 60 second epoch level data files provided by UK Biobank in csv format, Actiwatch in csv format, and Actiwatch in awd format. For the Actiwatch data functionality has been tested with example data from only one research group. Please let us know if you have difficulties using this functionality in your data.

### maxRecordingInterval

Numeric (default = NULL). To indicate the maximum gap in hours between repeated measurements with the same ID for the recordings to be appended. So, the assumption is that the ID can be matched, make sure argument idloc is set correctly. If argument maxRecordingInterval is set to NULL (default) recordings are not appended. If recordings overlap then GGIR will use the data from the latest recording. If recordings are seperated then the timegap between the recordings is filled with data points that resembale monitor not worn. The maximum value of maxFile gap is 504 (21 days). Only recordings from the same accelerometer brand are appended. The part 2 csv report will show number of appended recordings, sampling rate for each, time overlap or gap and the names of the filenames of the respective recording.

### extEpochData_timeformat

Character (default = “%d-%m-%Y %H:%M:%S”). To specify the time format used in the external epoch level data when argument dataFormat is set to “actiwatch_csv”, “actiwatch_awd”, or “actigraph_csv”. For example “%Y-%m-%d %I:%M:%S %p” for “2023-07-11 01:24:01 PM” or “%m/%d/%Y %H:%M:%S” “2023-07-11 13:24:01”

## 2.3 Raw Data Parameters

### chunksize

Numeric (default = 1). Value between 0.2 and 1 to specificy the size of chunks to be loaded as a fraction of a 12 hour period, e.g., 0.5 equals 6 hour chunks, 1 equals 12 hour chunks. For machines with less than 4Gb of RAM memory a value below 1 is recommended.

### spherecrit

Numeric (default = 0.3). The minimum required acceleration value (in g) on both sides of 0 g for each axis. Used to judge whether the sphere is sufficiently populated

### minloadcrit

Numeric (default = 72). The minimum number of hours the code needs to read for the autocalibration procedure to be effective (only sensitive to multitudes of 12 hrs, other values will be ceiled). After loading these hours only extra data is loaded if calibration error has not been reduced to under 0.01 g.

### printsummary

Boolean (default = FALSE). If TRUE will print a summary of the calibration procedure in the console when done.

### do.cal

Boolean (default = TRUE). Whether to apply auto-calibration or not by g.calibrate. Recommended setting is TRUE.

### backup.cal.coef

Character (default = “retrieve”). Option to use backed-up calibration coefficient instead of deriving the calibration coefficients when analysing the same file twice. Argument backup.cal.coef has two usecase. Use case 1: If the auto-calibration fails then the user has the option to provide back-up calibration coefficients via this argument. The value of the argument needs to be the name and directory of a csv-spreadsheet with the following column names and subsequent values: “filename” with the names of accelerometer files on which the calibration coefficients need to be applied in case auto-calibration fails; “scale.x”, “scale.y”, and “scale.z” with the scaling coefficients; “offset.x”, “offset.y”, and “offset.z” with the offset coefficients, and; “temperature.offset.x”, “temperature.offset.y”, and “temperature.offset.z” with the temperature offset coefficients. This can be useful for analysing short lasting laboratory experiments with insufficient sphere data to perform the auto-calibration, but for which calibration coefficients can be derived in an alternative way. It is the users responsibility to compile the csv-spreadsheet. Instead of building this file the user can also Use case 2: The user wants to avoid performing the auto-calibration repeatedly on the same file. If backup.cal.coef value is set to “retrieve” (default) then GGIR will look out for the “data_quality_report.csv” file in the outputfolder QC, which holds the previously generated calibration coefficients. If you do not want this happen, then deleted the data_quality_report.csv from the QC folder or set it to value “redo”.

### dynrange

Numeric (default = NULL). Provide dynamic range of 8 gravity.

### minimumFileSizeMB

Numeric (default = 2). Minimum File size in MB required to enter processing. This argument can help to avoid having short uninformative files to enter the analyses. Given that a typical accelerometer collects several MBs per hour, the default setting should only skip the very tiny files.

### rmc.dec

Character (default = “.”). Decimal used for numbers, same as dec argument in [utils]read.csv and in [data.table]fread.

### rmc.firstrow.acc

Numeric (default = NULL). First row (number) of the acceleration data.

### rmc.firstrow.header

Numeric (default = NULL). First row (number) of the header. Leave blank if the file does not have a header.

### rmc.header.length

Numeric (default = NULL). If file has header, specify header length (number of rows).

### rmc.col.acc

Numeric, three values (default = c(1, 2, 3)). Vector with three column (numbers) in which the acceleration signals are stored.

### rmc.col.temp

Numeric (default = NULL). Scalar with column (number) in which the temperature is stored. Leave in default setting if no temperature is avaible. The temperature will be used by g.calibrate.

### rmc.col.time

Numeric (default = NULL). Scalar with column (number) in which the timestamps are stored. Leave in default setting if timestamps are not stored.

### rmc.unit.acc

Character (default = “g”). Character with unit of acceleration values: “g”, “mg”, or “bit”.

### rmc.unit.temp

Character (default = “C”). Character with unit of temperature values: (K)elvin, (C)elsius, or (F)ahrenheit.

### rmc.unit.time

Character (default = “POSIX”). Character with unit of timestamps: “POSIX”, “UNIXsec” (seconds since origin, see argument rmc.origin), “character”, or “ActivPAL” (exotic timestamp format only used in the ActivPAL activity monitor).

### rmc.format.time

Character (default = “%Y-%m-%d %H:%M:%OS”). Character giving a date-time format as used by [base]strptime. Only used for rmc.unit.time: character and POSIX.

### rmc.bitrate

Numeric (default = NULL). If unit of acceleration is a bit then provide bit rate, e.g., 12 bit.

### rmc.dynamic_range

Numeric or character (default = NULL). If unit of acceleration is a bit then provide dynamic range deviation in g from zero, e.g., +/-6g would mean this argument needs to be 6. If you give this argument a character value the code will search the file header for elements with a name equal to the character value and use the corresponding numeric value next to it as dynamic range.

### rmc.unsignedbit

Boolean (default = TRUE). If unsignedbit = TRUE means that bits are only positive numbers. if unsignedbit = FALSE then bits are both positive and negative.

### rmc.origin

Character (default = “1970-01-01”). Origin of time when unit of time is UNIXsec, e.g., 1970-1-1.

### rmc.desiredtz

Character (default = NULL). Timezone in which experiments took place. This argument is scheduled to be deprecated and is now used to overwrite desiredtz if not provided.

### rmc.configtz

Character (default = NULL). Timezone in which device was configured. This argument is scheduled to be deprecated and is now used to overwrite configtz if not provided.

### rmc.sf

Numeric (default = NULL). Sample rate in Hertz, if this is stored in the file header then that will be used instead (see argument rmc.headername.sf).

### rmc.headername.sf

Character (default = NULL). If file has a header: Row name under which the sample frequency can be found.

### rmc.headername.sn

Character (default = NULL). If file has a header: Row name under which the serial number can be found.

### rmc.headername.recordingid

Character (default = NULL). If file has a header: Row name under which the recording ID can be found.

### rmc.header.structure

Character (default = NULL). Used to split the header name from the header value, e.g., “:” or ” “.

### rmc.check4timegaps

Boolean (default = FALSE). To indicate whether gaps in time should be imputed with zeros. Some sensing equipment provides accelerometer with gaps in time. The rest of GGIR is not designed for this, by setting this argument to TRUE the gaps in time will be filled with zeros.

### rmc.noise

Numeric (default = 13). Noise level of acceleration signal in m-units, used when working ad-hoc .csv data formats using read.myacc.csv. The read.myacc.csv does not take rmc.noise as argument, but when interacting with GGIR or g.part1 rmc.noise is used.

### rmc.col.wear

Numeric (default = NULL). If external wear detection outcome is stored as part of the data then this can be used by GGIR. This argument specifies the column in which the wear detection (Boolean) is stored.

### rmc.doresample

Boolean (default = FALSE). To indicate whether to resample the data based on the available timestamps and extracted sample rate from the file header.

### interpolationType

Integer (default = 1). To indicate type of interpolation to be used when resampling time series (mainly relevant for Axivity sensors), 1=linear, 2=nearest neighbour.

### imputeTimegaps

Boolean (default = TRUE). To indicate whether timegaps larger than 1 sample should be imputed. Currently only used for .gt3x data and ActiGraph .csv format, where timegaps can be expected as a result of Actigraph’s idle sleep.mode configuration.

### frequency_tol

Number (default = 0.1) as passed on to readAxivity from the GGIRread package. Represents the frequency tolerance as fraction between 0 and 1. When the relative bias per data block is larger than this fraction then the data block will be imputed by lack of movement with gravitational oriationed guessed from most recent valid data block. Only applicable to Axivity .cwa data.

### rmc.scalefactor.acc

Numeric value (default 1) to scale the acceleration signals via multiplication. For example, if data is provided in m/s2 then by setting this to 1/9.81 we would derive gravitational units.

## 2.4 Metrics Parameters

### do.anglex

Boolean (default = FALSE). If TRUE, calculates the angle of the X axis relative to the horizontal: = (^-1_rollmedian(x)(acc_rollmedian(y))^2 + (acc_rollmedian(z))^2) * 180/

### do.angley

Boolean (default = FALSE). If TRUE, calculates the angle of the Y axis relative to the horizontal: = (^-1_rollmedian(y)(acc_rollmedian(x))^2 + (acc_rollmedian(z))^2) * 180/

### do.anglez

Boolean (default = TRUE). If TRUE, calculates the angle of the Z axis relative to the horizontal: = (^-1_rollmedian(z)(acc_rollmedian(x))^2 + (acc_rollmedian(y))^2) * 180/

### do.zcx

Boolean (default = FALSE). If TRUE, calculates metric zero-crossing count for x-axis. For computation specifics see source code of function g.applymetrics

### do.zcy

Boolean (default = FALSE). If TRUE, calculates metric zero-crossing count for y-axis. For computation specifics see source code of function g.applymetrics

### do.zcz

Boolean (default = FALSE). If TRUE, calculates metric zero-crossing count for z-axis. For computation specifics see source code of function g.applymetrics

### do.enmo

Boolean (default = TRUE). If TRUE, calculates the metric: = _x^2 + acc_y^2 + acc_z^2 - 1 (if ENMO < 0, then ENMO = 0).

### do.lfenmo

Boolean (default = FALSE). If TRUE, calculates the metric ENMO over the low-pass filtered accelerations (for computation specifics see source code of function g.applymetrics). The filter bound is defined by the parameter hb.

### do.en

Boolean (default = FALSE). If TRUE, calculates the Euclidean Norm of the raw accelerations: = _x^2 + acc_y^2 + acc_z^2

### do.mad

Boolean (default = FALSE). If TRUE, calculates the Mean Amplitude Deviation: = 1n|r_i - |

### do.enmoa

Boolean (default = FALSE). If TRUE, calculates the metric: = _x^2 + acc_y^2 + acc_z^2 - 1 (if ENMOa < 0, then ENMOa = |ENMOa|).

### do.roll_med_acc_x

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

### do.roll_med_acc_y

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

### do.roll_med_acc_z

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

### do.dev_roll_med_acc_x

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

### do.dev_roll_med_acc_y

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

### do.dev_roll_med_acc_z

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

### do.bfen

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

### do.hfen

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

### do.hfenplus

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

### do.lfen

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

### do.lfx

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

### do.lfy

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

### do.lfz

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

### do.hfx

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

### do.hfy

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

### do.hfz

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

### do.bfx

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

### do.bfy

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

### do.bfz

Boolean (default = FALSE). If TRUE, calculates the metric. For computation specifics see source code of function g.applymetrics.

### do.brondcounts

Boolean (default = FALSE). THIS OPTION HAS BEEN DEPRECATED (October2022) DUE TO ISSUES WITH THE ACTIVITYCOUNTS PACKAGE, WE WILL ADD THIS BACK IN ONCE THE ISSUES WITH THE ACTIVITYCOUNTS PACKAGE ARE ADDRESSED. If TRUE, calculates the metric via R package activityCounts. We call them BrondCounts because there are large number of acitivty counts in the physical activity and sleep research field. By calling them brondcounts we clarify that these are the counts proposed by Jan Brønd and implemented in R by Ruben Brondeel. The brondcounts are intended to be an imitation of the counts produced by one of the closed source ActiLife software by ActiGraph.

### do.neishabouricounts

Boolean (default = FALSE). If TRUE, calculates the metric via R package actilifecounts, which is an implementation of the algorithm used in the closed-source software ActiLife by ActiGraph (methods published in doi: 10.1038/s41598-022-16003-x). We use the name of the first author (instead of ActiLifeCounts) of the paper and call them NeishabouriCount under the uncertainty that ActiLife will implement this same algorithm over time. To use the Neishabouri counts for the physical activity intensity classification in part 5 (i.e., metric over the threshold.lig, threshold.mod, and threshold.vig would be applied), the acc.metric argument needs to be set as one of the following: “NeishabouriCount_x”, “NeishabouriCount_y”, “NeishabouriCount_z”, “NeishabouriCount_vm” to use the counts in the x-, y-, z-axis or vector magnitude, respectively.

### hb

Numeric (default = 15). Higher boundary of the frequency filter (in Hertz) as used in the filter-based metrics.

### lb

Numeric (default = 0.2). Lower boundary of the frequency filter (in Hertz) as used in the filter-based metrics.

### n

Numeric (default = n). Order of the frequency filter as used in the filter-based metrics.

### zc.lb

Numeric (default = 0.25). Used for zero-crossing counts only. Lower boundery of cut-off frequency filter.

### zc.hb

Numeric (default = 3). Used for zero-crossing counts only. Higher boundery of cut-off frequencies in filter.

### zc.sb

Numeric (default = 0.01). Stop band used for calculation of zero crossing counts. Value is the acceleration threshold in g units below which acceleration will be rounded to zero.

### zc.order

Numeric (default = 2). Used for zero-crossing counts only. Order of frequency filter.

### zc.scale

Numeric (default = 1) Used for zero-crossing counts only. Scaling factor to be applied after counts are calculated (GGIR part 3).

### actilife_LFE

Boolean (default = FALSE). If TRUE, calculates the NeishabouriCount metric with the low-frequency extension filter as proposed in the closed source ActiLife software by ActiGraph. Only applicable to the metric NeishabouriCount.

## 2.5 Cleaning Parameters

### includedaycrit

Numeric (default = 16). Minimum required number of valid hours in day specific analysis (NOTE: there is no minimum required number of hours per day in the summary of an entire measurement, every available hour is used to make the best possible inference on average metric value per average day).

### ndayswindow

Numeric (default = 7). If strategy is set to 3 or 5, then this is the size of the window as a number of days. For strategy 3 value can be fractional, e.g. 7.5, while for strategy 5 it needs to be an integer.

### strategy

Numeric (default = 1). How to deal with knowledge about study protocol. strategy = 1 means select data based on hrs.del.start and hrs.del.end. strategy = 2 makes that only the data between the first midnight and the last midnight is used. strategy = 3 selects the most active X days in the file where X is specified by argument ndayswindow, where the days are a series of 24-h blocks starting any time in the day (X hours at the begining and end of this period can be deleted with arguments hrs.del.start and hrs.del.end) strategy = 4 to only use the data after the first midnight. strategy = 5 is similar to strategy = 3, but it selects X complete calendar days where X is specified by argument ndayswindow (X hours at the begining and end of this period can be deleted with arguments hrs.del.start and hrs.del.end).

### maxdur

Numeric (default = 0). How many DAYS after start of experiment did experiment definitely stop? (set to zero if unknown).

### hrs.del.start

Numeric (default = 0). How many HOURS after start of experiment did wearing of monitor start? Used in GGIR g.part2 when strategy = 1.

### hrs.del.end

Numeric (default = 0). How many HOURS before the end of the experiment did wearing of monitor definitely end? Used in GGIR g.part2 when strategy = 1.

### includedaycrit.part5

Numeric (default = 2/3). Inclusion criteria for number of valid hours, either as expressed as a ratio of 1 or as the number of hours in a 24 hour day.

### excludefirstlast.part5

Boolean (default = FALSE). If TRUE then the first and last window (waking-waking or midnight-midnight) are ignored in g.part5.

### TimeSegments2ZeroFile

Data frame (default = NULL). Optional data.frame to specify which time segments need to be ignored for the imputation, and acceleration metrics to be imputed by zeros. The data.frame is expected to contain two columns named windowstart and windowend, with the start- and end time of the time segment in POSIXlt class.

### do.imp

Boolean (default = TRUE). Whether to impute missing values (e.g., suspected of monitor non-wear or clippling) or not by g.impute in GGIR g.part2. Recommended setting is TRUE.

### data_cleaning_file

Character (default = NULL). Optional path to a csv file you create that holds four columns: ID, day_part5, relyonguider_part4, and night_part4. ID should hold the participant ID. Columns day_part5 and night_part4 allow you to specify which day(s) and night(s) need to be excluded from g.part5 and g.part4, respectively. So, this will be done regardless of whether the rest of GGIR thinks those day(s)/night(s) are valid. Column relyonguider_part4 allows you to specify for which nights g.part4 should fully rely on the guider. See also package vignette.

### minimum_MM_length.part5

Numeric (default = 23). Minimum length in hours of a MM day to be included in the cleaned g.part4 results.

### excludefirstlast

Boolean (default = FALSE). If TRUE then the first and last night of the measurement are ignored for the sleep assessment in g.part4.

### includenightcrit

Numeric (default = 16). Minimum number of valid hours per night (24 hour window between noon and noon), used for sleep assessment in g.part4.

### excludefirst.part4

Boolean (default = FALSE). If TRUE then the first night of the measurement are ignored for the sleep assessment in g.part4.

### excludelast.part4

Boolean (default = FALSE). If TRUE then the last night of the measurement are ignored for the sleep assessment in g.part4.

### max_calendar_days

Numeric (default = 0). The maximum number of calendar days to include (set to zero if unknown).

### nonWearEdgeCorrection

Boolean (default = TRUE). If TRUE then the non-wear detection around the edges of the recording (first and last 3 hours) are corrected following description in vanHees2013 as has been the default since then. This functionality is advisable when working with sleep clinic or exercise lab data typically lasting less than a day.

### nonwear_approach

Character (default = “2023”). Whether to use the traditional version of the non-wear detection algorithm (nonwear_approach = “2013”) or the new version (nonwear_approach = “2023”). The 2013 version would use the longsize window (windowsizes[3], one hour as default) to check the conditions for nonwear identification and would flag as nonwear the mediumsize window (windowsizes[2], 15 min as default) in the middle. The 2023 version differs in which it would flag as nonwear the full longsize window. For the 2013 method the longsize window is centered in the centre of the mediumsize window, while in the 2023 method the longsizewindow is aligned with its left edge to the left edge of the mediumsize window.

### segmentWEARcrit.part5

Numeric (default = 0.5). Fraction of qwindow segment expected to be valid in part 5, where 0.3 indicates that at least 30 percent of the time should be valid.

### segmentDAYSPTcrit.part5

Numeric vector or length 2 (default = c(0.9, 0)). Inclusion criteria for the proportion of the segment that should be classified as day (awake) and spt (sleep period time) to be considered valid. One of the two numbers should be 0, and the other defines the proportion of the segment that should be classified as day or spt, respectively. The default setting would focus on waking hour segments and includes all segments that overlap for at least 90 percent with waking hours. In order to shift focus to the SPT you could use c(0, 0.9) which ensures that all segments that overlap for at least 90 percent with the SPT are included. Setting both to zero would be problematic and is not allowed as that would introduce bias in behavioural estimates for the following reason: A complete segment would be averaged with an incomplete segments (someone going to bed or waking up in the middle of a segment) by which it is no longer clear whether the person is less active or sleeps more during that segment. Similarly it is not clear whether the person has more wakefulness during SPT for a segment or woke up or went to bed during the segment. Therefore, a minimum value of 0.5 is required but any value closer to 1 would be better.

## 2.6 Sleep Parameters

### anglethreshold

Numeric (default = 5). Angle threshold (degrees) for sustained inactivity periods detection. The algorithm will look for periods of time (timethreshold) in which the angle variability is lower than anglethreshold. This can be specified as multiple thresholds, each of which will be implemented, e.g., anglethreshold = c(5,10).

### timethreshold

Numeric (default = 5). Time threshold (minutes) for sustained inactivity periods detection. The algorithm will look for periods of time (timethreshold) in which the angle variability is lower than anglethreshold. This can be specified as multiple thresholds, each of which will be implemented, e.g., timethreshold = c(5,10).

### ignorenonwear

Boolean (default = TRUE). If TRUE then ignore detected monitor non-wear periods to avoid confusion between monitor non-wear time and sustained inactivity.

### constrain2range

Boolean (default = TRUE). Whether or not to constrain the range of threshold used in the diary free sleep period time window detection.

### HASPT.algo

Character (default = “HDCZA”). To indicate what algorithm should be used for the sleep period time detection. Default “HDCZA” is Heuristic algorithm looking at Distribution of Change in Z-Angle as described in van Hees et al. 2018. Other options included: “HorAngle”, which is based on HDCZA but replaces non-movement detection of the HDCZA algorithm by looking for time segments where the angle of the longitudinal sensor axis has an angle relative to the horizontal plane between -45 and +45 degrees.

### HASIB.algo

Character (default = “vanHees2015”). To indicate which algorithm should be used to define the sustained inactivity bouts (i.e., likely sleep). Options: “vanHees2015”, “Sadeh1994”, “Galland2012”.

### Sadeh_axis

Character (default = “Y”). To indicate which axis to use for the Sadeh1994 algorithm, and other algortihms that relied on count-based Actigraphy such as Galland2012.

### longitudinal_axis

Integer (default = NULL). To indicate which axis is the longitudinal axis. If not provided, the function will estimate longitudinal axis. Only used when sensor.location = “hip” or HASPT.algo = “HorAngle”.

### HASPT.ignore.invalid

Boolean (default = FALSE). To indicate whether invalid time segments should be ignored in the Sleep Period Time detection.

### loglocation

Character (default = NULL). Path to csv file with sleep log information. See package vignette for how to format this file.

### colid

Numeric (default = 1). Column number in the sleep log spreadsheet in which the participant ID code is stored.

### coln1

Numeric (default = 2). Column number in the sleep log spreadsheet where the onset of the first night starts.

### nnights

Numeric (default = NULL). This argument has been deprecated.

### relyonguider

Boolean (default = FALSE). If TRUE, then sleep onset and waking time are defined based on timestamps derived from the guider. If participants were instructed NOT to wear the accelerometer during waking hours then set to TRUE, in all other scenarios set to FALSE.

### def.noc.sleep

Numeric (default = 1). The time window during which sustained inactivity will be assumed to represent sleep, e.g., def.noc.sleep = c(21, 9). This is only used if no sleep log entry is available. If left blank def.noc.sleep = c() then the 12 hour window centred at the least active 5 hours of the 24 hour period will be used instead. Here, L5 is hardcoded and will not change by changing argument winhr in function g.part2. If def.noc.sleep is filled with a single integer, e.g., def.noc.sleep=c(1) then the window will be detected with based on built in algorithms. See argument HASPT.algo from HASPT for specifying which of the algorithms to use.

### sleeplogsep

Character (default = NULL). This argument is deprecated.

### sleepwindowType

Character (default = “SPT”). To indicate type of information in the sleeplog, “SPT” for sleep period time. Set to “TimeInBed” if sleep log recorded time in bed to enable calculation of sleep latency and sleep efficiency.

### possible_nap_window

Numeric (default = c(9, 18)). Numeric vector of length two with range in clock hours during which naps are assumed to take place, e.g., possible_nap_window = c(9, 18). Currently used in the context of an explorative nap classification algortihm that was trained in 3.5 year olds.

### possible_nap_dur

Numeric (default = c(15, 240)). Numeric vector of length two with range in duration (minutes) of a nap, e.g., possible_nap_dur = c(15, 240). Currently used in the context of an explorative nap classification algortihm that was trained in 3.5 year olds.

### nap_model

Character (default = NULL). To specify classification model. Currently the only option is “hip3yr”, which corresponds to a model trained with hip data in 3-3.5 olds trained with parent diary data.

### sleepefficiency.metric

Numeric (default = 1). If 1 (default), sleep efficiency is calculated as detected nocturnal sleep time divided by log-derived duration in sleep period time (considering user-reported sleep onset and wake times). If 2, sleep efficiency is calculated as detected nocturnal sleep time divided by detected duration in sleep period time plus sleep latency (where sleep latency refers to the difference between log-derived sleep onset and accelerometer-detected sleep onset).

### possible_nap_edge_acc

Minimum acceleration before or after the SIB for the nap to be considered.

## 2.7 Physical activity Parameters

### mvpathreshold

Numeric (default = 100). Acceleration threshold for MVPA estimation in GGIR g.part2. This can be a single number or an array of numbers, e.g., mvpathreshold = c(100, 120). In the later case the code will estimate MVPA seperately for each threshold. If this variable is left blank, e.g., mvpathreshold = c(), then MVPA is not estimated.

### boutcriter

Numeric (default = 0.8). A number between 0 and 1, it defines what fraction of a bout needs to be above the mvpathreshold, only used in GGIR g.part2.

### mvpadur

Numeric (default = 10). The bout duration(s) for which MVPA will be calculated. Only used in GGIR g.part2.

### boutcriter.in

Numeric (default = 0.9). A number between 0 and 1, it defines what fraction of a bout needs to be below the threshold.lig.

### boutcriter.lig

Numeric (default = 0.8). A number between 0 and 1, it defines what fraction of a bout needs to be between the threshold.lig and the threshold.mod.

### boutcriter.mvpa

Numeric (default = 0.8). A number between 0 and 1, it defines what fraction of a bout needs to be above the threshold.mod.

### threshold.lig

Numeric (default = 40). In g.part5: Threshold for light physical activity to separate inactivity from light. Value can be one number or an array of multiple numbers, e.g., threshold.lig =c(30,40). If multiple numbers are entered then analysis will be repeated for each combination of threshold values. Threshold is applied to the first metric in the milestone data, so if you have only specified do.enmo = TRUE then it will be applied to ENMO.

### threshold.mod

Numeric (default = 100). In g.part5: Threshold for moderate physical activity to separate light from moderate. Value can be one number or an array of multiple numbers, e.g., threshold.mod = c(100, 120). If multiple numbers are entered then analysis will be repliced for each ombination of threshold values. Threshold is applied to the first metric in the milestone data, so if you have only specified do.enmo = TRUE then it will be applied to ENMO.

### threshold.vig

Numeric (default = 400). In g.part5: Threshold for vigorous physical activity to separate moderate from vigorous. Value can be one number or an array of multiple numbers, e.g., threshold.vig =c(400,500). If multiple numbers are entered then analysis will be repliced for each combination of threshold values. Threshold is applied to the first metric in the milestone data, so if you have only specified do.enmo = TRUE then it will be applied to ENMO.

### boutdur.mvpa

Numeric (default = c(1, 5, 10)). Duration(s) of MVPA bouts in minutes to be extracted. It will start with the identification of the longest to the shortest duration. In the default setting, it will start with the 10 minute bouts, followed by 5 minute bouts in the rest of the data, and followed by 1 minute bouts in the rest of the data.

### boutdur.in

Numeric (default = c(10, 20, 30)). Duration(s) of inactivty bouts in minutes to be extracted. Inactivity bouts are detected in the segments of the data which were not labelled as sleep or MVPA bouts. It will start with the identification of the longest to the shortest duration. In the default setting, it will start with the identification of 30 minute bouts, followed by 20 minute bouts in the rest of the data, and followed by 10 minute bouts in the rest of the data. Note that we use the term inactivity instead of sedentary behaviour for the lowest intensity level of behaviour. The reason for this is that GGIR does not attempt to classifying the activity type sitting at the moment, by which we feel that using the term sedentary behaviour would fail to communicate that.

### boutdur.lig

Numeric (default = c(1, 5, 10)). Duration(s) of light activty bouts in minutes to be extracted. Light activity bouts are detected in the segments of the data which were not labelled as sleep, MVPA, or inactivity bouts. It will start with the identification of the longest to the shortest duration. In the default setting, this will start with the identification of 10 minute bouts, followed by 5 minute bouts in the rest of the data, and followed by 1 minute bouts in the rest of the data.

### frag.metrics

Character (default = NULL). Fragmentation metric to exract. Can be “mean”, “TP”, “Gini”, “power”, or “CoV”, “NFragPM”, or all the above metrics with “all”. See package vignette for description of fragmentation metrics.

## 2.8 24/7 Parameters

### qwindow

Numeric or character (default = c(0, 24)). To specify windows over which all variables are calculated, e.g., acceleration distribution, number of valid hours, LXMX analysis, MVPA. If numeric, qwindow should have length two, e.g., qwindow = c(0, 24), all variables will only be calculated over the full 24 hours in a day. If qwindow = c(8, 24) variables will be calculated over the window 0-8, 8-24 and 0-24. All days in the recording will be segmented based on these values. If you want to use a day specific segmentation in each day then you can set qwindow to be the full path to activity diary file (character). Expected format of the activity diary is: First column headers followed by one row per recording, first column is recording ID, which needs to match with the ID GGIR extracts from the accelerometer file. Followed by date column in format “23-04-2017”, where date format is specified by argument qwindow_dateformat (below). Use the character combination date, Date or DATE in the column name. This is followed by one or multiple columns with start times for the activity types in that day format in hours:minutes:seconds. The header of the column will be used as label for each activity type. Insert a new date column before continuing with activity types for next day. Leave missing values empty. If an activitylog is used then individuals who do not appear in the activitylog will still be processed with value qwindow = c(0, 24). Dates with no activiy log data can be skipped, no need to have a column with the date followed by a column with the next date. If times in the activity diary are not multiple of the short window size (epoch length), the next epoch is considered (e.g., with epoch of 5 seconds, 8:00:02 will be redefined as 8:00:05 in the activity log). When using the qwindow functionality in combination with GGIR part 5 then make sure to check that arguments segmentWEARcrit.part5 and segmentDAYSPTcrit.part5 are specfied to your research needs.

### qlevels

Numeric (default = NULL). Array of percentiles for which value needs to be extracted. These need to be expressed as a fraction of 1, e.g., c(0.1, 0.5, 0.75). There is no limit to the number of percentiles. If left empty then percentiles will not be extracted. Distribution will be derived from short epoch metric data. Argument qlevels can for example be used for the MX-metrics (e.g. Rowlands et al) as discussed in the ://cran.r-project.org/package=GGIR/vignettes/GGIR.htmlmain package vignette

### qwindow_dateformat

Character (default = “%d-%m-%Y”). To specify the date format used in the activity log as used by [base]strptime.

### ilevels

Numeric (default = NULL). Levels for acceleration value frequency distribution in m, e.g., ilevels = c(0,100,200). There is no limit to the number of levels. If left empty then the intensity levels will not be extracted. Distribution will be derived from short epoch metric data.

### IVIS_windowsize_minutes

Numeric (default = 60). Window size of the Intradaily Variability (IV) and Interdaily Stability (IS) metrics in minutes, needs to be able to add up to 24 hours.

### IVIS_epochsize_seconds

Numeric (default = NULL). This argument is deprecated.

### IVIS.activity.metric

Numeric (default = 2). Metric used for activity calculation. Value = 1, uses continuous scaled acceleration. Value = 2, tries to collapse acceleration into a binary score of rest versus active to try to similate the original approach.

### IVIS_acc_threshold

Numeric (default = 20). Acceleration threshold to distinguish inactive from active.

### qM5L5

Numeric (default = NULL). Percentiles (quantiles) to be calculated over L5 and M5 window.

### MX.ig.min.dur

Numeric (default = 10). Minimum MX duration needed in order for intensity gradient to be calculated.

### M5L5res

Numeric (default = 10). Resolution of L5 and M5 analysis in minutes.

### winhr

Numeric (default = 5). Vector of window size(s) (unit: hours) of L5 and M5 analysis.

### iglevels

Numeric (default = NULL). Levels for acceleration value frequency distribution in mused for intensity gradient calculation (according to the method by Rowlands 2018). By default this is argument is empty and the intensity gradient calculation is not done. The user can either provide a single value (any) to make the intensity gradient use the bins iglevels = c(seq(0,4000,by=25), 8000) or the user could specify their own distribution. There is no constriction to the number of levels.

### LUXthresholds

Numeric (default = c(0, 100, 500, 1000, 3000, 5000, 10000)). Vector with numeric sequece corresponding to the thresholds used to calculate time spent in LUX ranges.

### LUX_cal_constant

Numeric (default = NULL). If both LUX_cal_constant and LUX_cal_exponent are provided LUX values are converted based on formula y = constant * exp(x * exponent)

### LUX_cal_exponent

Numeric (default = NULL). If both LUX_cal_constant and LUX_cal_exponent are provided LUX LUX values are converted based on formula y = constant * exp(x * exponent)

### LUX_day_segments

Numeric (default = NULL). Vector with hours at which the day should be segmented for the LUX analysis.

### window.summary.size

Numeric (default = 10). Functionality designed for the London Centre of Longidutinal studies. Size in minutes of the summary window

### L5M5window

Argument deprecated after version 1.5-24. This argument used to define the start and end time, in 24 hour clock hours, over which L5M5 needs to be calculated. Now this is done with argument qwindow.

### cosinor

Boolean (default = FALSE). Whether to apply the cosinor analysis from the ActCR package.

## 2.9 Output Parameters

### epochvalues2csv

Boolean (default = FALSE). In g.part2: If TRUE then epoch values are exported to a csv file. Here, non-wear time is imputed where possible.

### save_ms5rawlevels

Boolean (default = FALSE). In g.part5: Whether to save the time series classification (levels) as csv or RData files (as defined by save_ms5raw_format).

### save_ms5raw_format

Character (default = “csv”). In g.part5: To specify how data should be stored: either “csv” or “RData”. Only used if save_ms5rawlevels = TRUE.

### save_ms5raw_without_invalid

Boolean (default = TRUE). In g.part5: To indicate whether to remove invalid days from the time series output files. Only used if save_ms5rawlevels = TRUE.

### storefolderstructure

Boolean (default = FALSE). Store folder structure of the accelerometer data.

### timewindow

Character (default = c(“MM”, “WW”)). In g.part5: Timewindow over which summary statistics are derived. Value can be “MM” (midnight to midnight), “WW” (waking time to waking time), “OO” (sleep onset to sleep onset), or any combination of them.

### viewingwindow

Numeric (default = 1). Centre the day as displayed around noon (viewingwindow = 1) or around midnight (viewingwindow = 2) in the visual report generated with visualreport = TRUE.

### dofirstpage

Boolean (default = TRUE). To indicate whether a first page with histograms summarizing the whole measurement should be added in the file summary reports generated with visualreport = TRUE.

### visualreport

Boolean (default = TRUE). If TRUE, then generate visual report based on combined output from g.part2 and g.part4. Please note that the visual report was initially developed to provide something to show to study participants and not for data quality checking purposes. Over time we have improved the visual report to also be useful for QC-ing the data. However, some of the scorings as shown in the visual report are created for the visual report only and may not reflect the scorings in the main GGIR analyses as reported in the quantitative csv-reports. Most of our effort in the past 10 years has gone into making sure that the csv-report are correct, while the visualreport has mostly been a side project. This is unfortunate and we hope to find funding in the future to design a new report specifically for the purpose of QC-ing the anlayses done by GGIR.

### week_weekend_aggregate.part5

Boolean (default = FALSE). In g.part5: To indicate whether week and weekend-days aggregates should be stored. This is turned off by default as it generates a large number of extra columns in the output report.

### do.part3.pdf

Boolean (default = TRUE). In g.part3: Whether to generate a pdf for g.part3.

### outliers.only

Boolean (default = FALSE). In g.part4: Only used if do.visual = TRUE. If FALSE, all available nights are included in the visual representation of the data and sleeplog. If TRUE, then only nights with a difference in onset or waking time larger than the variable of argument criterror will be included.

### criterror

Numeric (default = 3). In g.part4: Only used if do.visual = TRUE and outliers.only = TRUE. criterror specifies the number of minimum number of hours difference between sleep log and accelerometer estimate for the night to be included in the visualisation.

### do.visual

Boolean (default = TRUE). In g.part4: If TRUE, the function will generate a pdf with a visual representation of the overlap between the sleeplog entries and the accelerometer detections. This can be used to visually verify that the sleeplog entries do not come with obvious mistakes.

### do.sibreport

Boolean (default = FALSE). In g.part4: To indicate whether to generate report for the sustained inactivity bouts (SIB). If set to TRUE and when an advanced sleep diary is available in part 4 then part 5 will use this to generate summary statistics on the overlap between self-reported nonwear and napping with SIB. Here, SIB cna be filter based on argument possible_nap_edge_acc and the first value of possible_nap_dur

### do.part2.pdf

Boolean (default = TRUE). In g.part2: Whether to generate a pdf for g.part2.

### sep_reports

Character (default = “,”). Value used as sep argument in [utils]write.csv for writing csv files containing the GGIR reports.

### sep_config

Character (default = “,”). Value used as sep argument in [utils]write.csv for writing the GGIR config file.

### visualreport_without_invalid

Boolean (default = TRUE). If TRUE, then reports generated with visualreport = TRUE only show the windows with sufficiently valid data according to includedaycrit when viewingwindow = 1 or includenightcrit when viewingwindow = 2